From participatory sensing to Mobile Crowd Sensing

descriptionPublicationkeyboard_double_arrow_right Article , Preprint , Conference object 01 Mar 2014Embargo end date: 01 Jan 2014Publisher:IEEEJournal:2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS)

Authors: Bin Guo 0001; Zhiwen Yu 0001; Daqing Zhang 0001; Xingshe Zhou 0001;

doi: 10.1109/percomw.2014.6815273 , 10.48550/arxiv.1401.3090

arXiv: 1401.3090

From participatory sensing to Mobile Crowd Sensing

- Summary
- Subjects
- Metrics

Abstract

The research on the efforts of combining human and machine intelligence has a long history. With the development of mobile sensing and mobile Internet techniques, a new sensing paradigm called Mobile Crowd Sensing (MCS), which leverages the power of citizens for large-scale sensing has become popular in recent years. As an evolution of participatory sensing, MCS has two unique features: (1) it involves both implicit and explicit participation; (2) MCS collects data from two user-participant data sources: mobile social networks and mobile sensing. This paper presents the literary history of MCS and its unique issues. A reference framework for MCS systems is also proposed. We further clarify the potential fusion of human and machine intelligence in MCS. Finally, we discuss the future research trends as well as our efforts to MCS.

Related Organizations

Northwestern Polytechnical University
China (People's Republic of)
IMT, Télécom SudParis
France

Keywords

FOS: Computer and information sciences, Computer Science - Computers and Society, Computers and Society (cs.CY), Computer Science - Human-Computer Interaction, Human-Computer Interaction (cs.HC)

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	281
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 1%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 1%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 0.1%